Vetted Grafana Professionals

Nischit Patel

Screened

Mid-level Software Development Engineer specializing in Python, APIs, and AWS

NC, US5y exp

“Backend engineer with experience modernizing legacy systems and building modular Python/Flask services, including a REST-to-GraphQL migration for an e-commerce platform that improved API response time by 45%. Strong in performance and scalability work across PostgreSQL/SQLAlchemy (indexing, JSONB, N+1 fixes, connection pooling) and high-throughput systems (Celery + Redis), plus integrating ML microservices with TorchServe, Kafka streaming, feature stores, and Prometheus/Grafana monitoring.”

Gly Vu

Screened

Junior Software Engineer specializing in full-stack tools and LLM inference infrastructure

3y exp

“Full-stack/edge-focused engineer who took a manual, terminal-based AI calibration workflow and turned it into a web-enabled remote calibration system designed for low-bandwidth 5G field deployments, now used across 85+ field sites. Experienced operating edge fleets with versioned rollouts, Kubernetes-based cloud monitoring, and Prometheus/Grafana observability, plus refactoring fast-moving AI codebases for modularity and strong typing.”

Revanth P

Screened

Mid-level DevOps Engineer specializing in AWS, Azure, Kubernetes, and GenAI infrastructure

Walnut Creek, CA4y exp

“Database/platform engineer with stronger hands-on experience in AWS and Azure than GCP, but able to speak credibly about cloud database architecture, automation, and reliability engineering. They led an on-prem MySQL to RDS/DynamoDB migration, built Terraform/Python-based zero-touch database operations, and described a performance incident where latency dropped from 2s to under 300ms while supporting 2x traffic.”

“Backend/AI engineer with deep healthcare experience building production Python microservices that turn raw clinical audio into structured notes and insights. They owned systems end-to-end across architecture, launch, monitoring, and incident response, with measurable impact including 40% lower operating costs, 22% better latency, and 99.9% uptime in a regulated environment.”

“Built an end-to-end agentic population health strategy copilot for healthcare leadership, turning broad chronic disease questions into structured, evidence-backed strategy briefs. Stands out for combining healthcare domain knowledge with production-grade GenAI implementation, including LangGraph orchestration, Databricks/MLflow deployment, human review, and quality gates focused on citations, metrics, risks, and safety.”

“Backend/AI engineer who built an enterprise RAG chatbot over 40,000+ technical documents, owning the system from ingestion and retrieval design through launch, optimization, and incident prevention. Stands out for treating LLM reliability as a data, retrieval, and observability problem—delivering 90%+ benchmark accuracy, ~50% fewer hallucinations, and major gains in lookup speed and latency.”

Nikita Sipeikin

Screened

Mid Backend Java Developer specializing in high-load FinTech systems

Bali, Indonesia5y exp

“Java developer with hands-on experience using AI coding tools heavily in day-to-day development, estimating roughly 60% AI-generated output in a recent shipped feature. Brings practical debugging and testing experience, including performance testing and resolving Spring Boot circular dependency issues through architectural changes.”

“Backend/distributed-systems engineer with 9 years of Go experience, focused on financial-services platforms where performance, reliability, and regulatory auditability are critical. He has built low-latency market data infrastructure (p99 under 8ms) and optimized compliance/reporting systems used by finance and audit teams, combining strong systems design with practical production operations.”

“Built AI-powered products across both healthcare and financial services, including a privacy-conscious assistant for elderly health check-ins and a production RAG system for high-stakes financial document analysis. Stands out for combining full-stack engineering with strong LLM reliability practices—grounding, structured outputs, fallback handling, monitoring, and human-in-the-loop controls—while delivering measurable impact on accuracy, speed, and system performance.”

“Built and owned an internal AI-powered knowledge assistant that centralized fragmented company knowledge across docs, tickets, and internal systems. They designed the backend, ingestion pipelines, vector search, RAG workflow, and APIs, then drove adoption through pilot testing and quality improvements—ultimately automating roughly 30% of support inquiries and cutting resolution time by about two hours per ticket.”

“Frontend engineer who led an internal React + TypeScript admin dashboard end-to-end, including a collaboratively owned component system and complex RBAC/permissions workflows. Emphasizes scalable architecture and correctness using React Query (mutations + query invalidation), centralized permission logic, and TypeScript for safe evolution, plus performance tuning for data-heavy tables and pragmatic MVP-driven delivery with gradual rollouts.”

“Product-minded full-stack engineer with hospitality tech experience who owned and scaled a multi-region guest verification/check-in workflow (ID/passport scanning, OCR, and government submissions) and built internal tools that cut manual entry up to 80%. Also built a React/TypeScript + FastAPI RAG “second brain” with async ingestion workers and an event-driven e-folio email microservice hardened with idempotency and retries.”

Rizwana Shaik

Screened

Mid-level Full-Stack Software Engineer specializing in cloud-native apps and AI copilots

Dallas, TX4y exp

“Internship project building and deploying a LLaMA-based, RAG-enabled copilot inside a Professional Services Automation platform, enabling natural-language navigation, text-to-SQL reporting, and project/resource/budget insights across multiple modules. Addressed real production issues like context drift and vague queries with hybrid search, metadata enrichment, and an intent classification/rewriting layer, orchestrated via Apache Airflow—ultimately cutting PMO reporting time by 40%.”

Tim Kwok

Screened

Senior DevOps Engineer specializing in multi-region AWS/GCP cloud infrastructure

United States8y exp

“Backend/data engineer with strong AWS production experience spanning FastAPI microservices and large-scale data pipelines. Has delivered containerized Python services on EKS with Terraform/Helm/GitHub Actions, implemented robust auth/secrets practices, and owned ETL reliability (Glue/S3/Redshift) including incident response and idempotent reruns. Demonstrated SQL tuning on 50M-record ETL workloads to remove SLA misses and improve reliability.”

Faisal Hayat

Screened

Senior Python Full-Stack Engineer specializing in AI-powered backend systems

Lahore, Pakistan6y exp

“Backend-leaning full-stack engineer with startup experience building AI-powered products and ERP/SaaS platforms. They’ve delivered Python/FastAPI/Django and React systems on AWS, including an AI document processing platform and business workflow tools used by thousands of users, with strong hands-on depth in database optimization and production operations.”

Xuebin Dong

Screened

Mid-level Software Engineer specializing in distributed systems and healthcare data platforms

San Diego, CA4y exp

“Full-stack and AI engineer who built both a large-scale clinical imaging dataset exploration engine and DMP Chef, an open-source LLM product for generating funder-compliant Data Management Plans. Stands out for combining strong product ownership, data-intensive backend architecture, and practical LLM systems work with retrieval, structured outputs, evals, and human-in-the-loop compliance safeguards.”

“BI developer and software engineer with experience spanning pre-funding startup work and production web applications. They built database/reporting foundations for an early-stage venture, later developed an AI-assisted regression testing tool in Python/Selenium, and worked on JavaScript dashboards plus bidirectional API integrations in ASP.NET Core systems.”

“ML/AI engineer with hands-on experience building healthcare and fraud-detection systems from experimentation through deployment, monitoring, and retraining. Stands out for combining real-time IoT pipelines, cloud-native MLOps, and GenAI/RAG in regulated healthcare settings, with reported impact including reduced emergency response times and a 25% reduction in manual diagnosis time.”

“Cloud infrastructure/DevOps engineer focused on AWS/Azure production environments, with hands-on experience running EKS-based platforms, Terraform-driven IaC, and secure Jenkins/GitLab CI pipelines. Has led real-world migrations from EC2 to Kubernetes using blue/green cutovers and executed multi-AZ failover testing with documented same-day recovery and no data loss; does not have direct IBM Power/AIX ownership experience.”